Azure ML Pipeline Input/Output Modes

DP-100 By islever Editorial Last updated

Short answer

ACE rw_mount upload direct

Question

You have an Azure Machine Learning workspace. You plan to run a job to train a model as an MLflow model output. You need to specify the output mode of the MLflow model. Which three modes can you specify? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
  1. rw_mount Correct Answer
  2. ro_mount
  3. upload Correct Answer
  4. download
  5. direct Correct Answer

Key Takeaways

Candidates must distinguish between input modes (mount vs. download) and output modes (mount vs. upload). The common trap is selecting 'download' or 'direct', which are not standard output modes for pipeline assets in this context.

This question tests knowledge of Azure Machine Learning pipeline data modes, specifically for MLflow model outputs. The community consensus and official documentation confirm that rw_mount, ro_mount, and upload are valid output modes.

Expert Analysis

Why the Answer Is Correct

According to Microsoft's Azure Machine Learning documentation, when specifying data inputs and outputs in a pipeline, you can define the mode. For outputs, the supported modes include 'upload' (default for many asset types), 'rw_mount' (read-write mount), and 'ro_mount' (read-only mount). These modes dictate how the data is handled during job execution.

Why the Other Options Are Wrong

'Download' is typically an input mode where data is downloaded to the compute target, not an output mode. 'Direct' is not a recognized mode for MLflow model outputs in the Azure ML pipeline specification. Selecting these indicates a confusion between input handling strategies and output storage mechanisms.

Community Comment Notes

Multiple users confirmed ACE as the correct answer, citing the official Microsoft Learn article on managing inputs and outputs. One comment provided a direct link to the documentation, reinforcing the validity of the rw_mount, ro_mount, and upload options. The high vote count (100%) suggests strong agreement within the exam preparation community.

Official Reference

Array

This link opens the official vendor documentation in a new tab.

Exam Strategy

Memorize the distinction between input modes (download, mount) and output modes (upload, mount). When dealing with MLflow models, remember that 'upload' is often the default for storing artifacts, while 'mount' modes are used for efficient access to large datasets or models during training.

Community Votes

ACE
100%

100% of anonymous learners picked answer ACE. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 5 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 5

  1. apz333 👍 3
    Seems correct: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-inputs-outputs-pipeline?view=azureml-api-2&tabs=cli#path-and-mode-for-data-inputsoutputs
  2. evangelist 👍 3
    correct answer: A. rw_mount B. ro_mount C. upload
  3. LM12 👍 1
    RW_mount uplaod direct https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-inputs-outputs-pipeline?view=azureml-api-2&tabs=cli
  4. D0ktor 👍 2
    Correct answer
  5. evangelist 👍 1
    correct

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